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89 lines
2.9 KiB
89 lines
2.9 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/inference/analysis/analyzer.h"
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#include <google/protobuf/text_format.h>
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#include <gtest/gtest.h>
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#include "paddle/fluid/inference/analysis/ut_helper.h"
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#include "paddle/fluid/inference/api/paddle_inference_api.h"
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#include "paddle/fluid/inference/api/paddle_inference_pass.h"
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namespace paddle {
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namespace inference {
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namespace analysis {
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using namespace framework; // NOLINT
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TEST(Analyzer, analysis_without_tensorrt) {
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FLAGS_IA_enable_tensorrt_subgraph_engine = false;
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Argument argument;
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argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
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Analyzer analyser;
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analyser.Run(&argument);
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}
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TEST(Analyzer, analysis_with_tensorrt) {
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FLAGS_IA_enable_tensorrt_subgraph_engine = true;
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Argument argument;
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argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
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Analyzer analyser;
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analyser.Run(&argument);
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}
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void TestWord2vecPrediction(const std::string &model_path) {
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NativeConfig config;
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config.model_dir = model_path;
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config.use_gpu = false;
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config.device = 0;
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auto predictor =
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::paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
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config);
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// One single batch
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int64_t data[4] = {1, 2, 3, 4};
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PaddleTensor tensor;
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tensor.shape = std::vector<int>({4, 1});
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tensor.data = PaddleBuf(data, sizeof(data));
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tensor.dtype = PaddleDType::INT64;
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// For simplicity, we set all the slots with the same data.
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std::vector<PaddleTensor> slots(4, tensor);
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std::vector<PaddleTensor> outputs;
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CHECK(predictor->Run(slots, &outputs));
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PADDLE_ENFORCE(outputs.size(), 1UL);
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// Check the output buffer size and result of each tid.
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PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
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float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
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0.000932706};
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const size_t num_elements = outputs.front().data.length() / sizeof(float);
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// The outputs' buffers are in CPU memory.
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for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
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LOG(INFO) << "data: "
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<< static_cast<float *>(outputs.front().data.data())[i];
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PADDLE_ENFORCE(static_cast<float *>(outputs.front().data.data())[i],
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result[i]);
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}
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}
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TEST(Analyzer, word2vec_without_analysis) {
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TestWord2vecPrediction(FLAGS_inference_model_dir);
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}
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} // namespace analysis
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} // namespace inference
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} // namespace paddle
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